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Minería de datos educativos para la detección de recursos clave
(UCOPress, 2017)
Este artículo describe un proyecto de innovación educativa centrado en diseñar y desarrollar un nuevo módulo de Moodle que permita obtener modelos predictivos basados en árboles de decisión a partir de los datos de uso ...
Multi-Objective Genetic Programming for Feature Extraction and Data Visualization
(2017)
Feature extraction transforms high dimensional
data into a new subspace of lower dimensionalitywhile keeping
the classification accuracy. Traditional algorithms do not
consider the multi-objective nature of this task. ...
A Classification Module for Genetic Programming Algorithms in JCLEC
(MIT Press, 2014)
JCLEC-Classi cation is a usable and extensible open source library for genetic program-
ming classi cation algorithms. It houses implementations of rule-based methods for clas-
si cation based on genetic programming, ...
ur-CAIM: Improved CAIM Discretization for Unbalanced and Balanced Data
(2015-10-15)
Supervised discretization is one of basic data preprocessing
techniques used in data mining. CAIM (Class-
Attribute InterdependenceMaximization) is a discretization
algorithm of data for which the classes are known. ...
Parallel evaluation of Pittsburgh rule-based classifiers on GPUs
(2017-01-19)
Individuals from Pittsburgh rule-based classifiers represent a complete solution
to the classification problem and each individual is a variable-length set
of rules. Therefore, these systems usually demand a high level ...
Speeding up Multiple Instance Learning Classification Rules on GPUs
(2017)
Multiple instance learning is a challenging task in supervised learning and data mining. How-
ever, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets.
Graphics processing ...
An Extended Approach of a Two-Stage Evolutionary Algorithm in Artificial Neural Networks for Multiclassification Tasks
(Springer, 2016)
This chapter considers a recent algorithm to add broader diversity at the beginning of the evolutionary process and extends it to sigmoidal neural networks. A simultaneous evolution of architectures and weights is performed ...
A two-stage algorithm in evolutionary product unit neural networks for classification
(Elsevier, 2011)
This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of ...